Design, Implementation, and Evaluation of an Online Systemic Human Anatomy Course with Laboratory
Bibliographic record
Abstract
Systemic Human Anatomy is a full credit, upper year undergraduate course with a prosection laboratory demonstration at Western University Canada. To meet enrolment demands beyond the physical space of the laboratory facility, a fully online section was developed to run concurrently with the traditional face-to-face (F2F) course in 2012-13. Lectures for F2F students were broadcast in live and archived format to online students using Blackboard Collaborate virtual classroom. Online laboratories were delivered in the virtual classroom by teaching assistants (TAs) with three dimensional (3D) anatomical models (Netter’s 3D Interactive Anatomy).\nStudent performance outcomes and student and instructor perceptions of the experience were studied over a two year period to determine the strengths and weaknesses of the new format. Data comparing the online and F2F student grades suggest that previous academic achievement, and not delivery format, predicts performance in anatomy. Students valued pace control, schedule and location flexibility of learning from archived materials. In the online laboratory, they had difficulty using the 3D models and preferred the unique and hands-on experiences of cadaveric specimens. The F2F environment was conducive to learning in both lecture and lab because students felt more engaged by instructors in person and were less distracted by their surroundings.\nThe course was modified in its second year with the addition of virtual breakout laboratory rooms, which allowed students to learn in smaller groups and interact with 3 TAs per lesson. The new laboratory format encouraged the majority of online students to use the 3D models. Virtual breakout rooms engaged online students in learning and the students were satisfied with their interactions with TAs and peers, though online laboratories did not adequately replace the F2F learning environment for all students. The biggest concern of the instructors was their inability to see coverbal student behaviour and use it to assess class engagement and their teaching effectiveness.\nThe design and evaluation of the course will guide anatomy educators in accommodating large student populations when faced with limited laboratory facilities and/or cadaveric specimens. The instructional methods will also be of interest to science, engineering, and mathematics educators who teach 3D concepts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".